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@ARTICLE{Farshian:1017988,
      author       = {Farshian, Anis and Götz, Markus and Cavallaro, Gabriele
                      and Debus, Charlotte and Nießner, Matthias and
                      Benediktsson, Jón Atli and Streit, Achim},
      title        = {{D}eep-{L}earning-{B}ased 3-{D} {S}urface
                      {R}econstruction—{A} {S}urvey},
      journal      = {Proceedings of the IEEE},
      volume       = {111},
      number       = {11},
      issn         = {0018-9219},
      reportid     = {FZJ-2023-04458},
      pages        = {1464 - 1501},
      year         = {2023},
      abstract     = {In the last decade, deep learning (DL) has significantly
                      impacted industry and science. Initially largely motivated
                      by computer vision tasks in 2-D imagery, the focus has
                      shifted toward 3-D data analysis. In particular, 3-D surface
                      reconstruction, i.e., reconstructing a 3-D shape from sparse
                      input, is of great interest to a large variety of
                      application fields. DL-based approaches show promising
                      quantitative and qualitative surface reconstruction
                      performance compared to traditional computer vision and
                      geometric algorithms. This survey provides a comprehensive
                      overview of these DL-based methods for 3-D surface
                      reconstruction. To this end, we will first discuss input
                      data modalities, such as volumetric data, point clouds, and
                      RGB, single-view, multiview, and depth images, along with
                      corresponding acquisition technologies and common benchmark
                      datasets. For practical purposes, we also discuss evaluation
                      metrics enabling us to judge the reconstructive performance
                      of different methods. The main part of the document will
                      introduce a methodological taxonomy ranging from point-and
                      mesh-based techniques to volumetric and implicit neural
                      approaches. Recent research trends, both methodological and
                      for applications, are highlighted, pointing toward future
                      developments.},
      cin          = {JSC},
      ddc          = {620},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
                      (SDLs) and Research Groups (POF4-511)},
      pid          = {G:(DE-HGF)POF4-5111},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:001103912800001},
      doi          = {10.1109/JPROC.2023.3321433},
      url          = {https://juser.fz-juelich.de/record/1017988},
}